This chapter presents advanced control strategies for Permanent Magnet Synchronous Motor (PMSM) drives, addressing the limitations of conventional methods such as PI controllers and Model Predictive Control (MPC) in terms of computational burden, steady-state accuracy, and transient response. By integrating machine learning (ML) techniques, particularly neural networks (NNs), the chapter proposes three innovative solutions. First, the NN-MPC-DSVPWM approach utilizes offline-trained NNs to reduce the execution time of conventional MPC with discrete space vector PWM, enabling efficient real-time implementation on DSPs without compromising performance. Second, a hybrid controller combining MPC and Field-Oriented Control (FOC) with NN-based switching methods achieves fast dynamic response, minimal steady-state error, and uniform switching through SVPWM. This method proves highly robust and effective through extensive validation. Finally, the Generalized Hybrid Method (GHM) extends control to multilevel inverters by integrating FOC, MPC, fuzzy logic (FL), and NN for rapid and intelligent decision-making. GHM effectively reduces overshoot and enhances system stability and adaptability. Together, these ML-driven strategies significantly enhance PMSM drive performance across diverse operating conditions, offering scalable and intelligent solutions for industrial automation, transportation.